root.dir <- here::here()
knitr::opts_chunk$set(echo = T, root.dir=root.dir)
knitr::opts_knit$set(root.dir=root.dir)

library(dplyr)
library(ggplot2)

library(echolocatoR)

Import fine-mapping results

Query fine-mapping portal

Download Alzheimer’s Disease GWAS fine-mapping results for the APOE locus via the echolocatoR Fine-mapping Portal API.

local_finemap <- echolocatoR::GITHUB.portal_query(dataset_types = "GWAS", 
                                                 phenotypes = "Alzheimer", 
                                                 LD_panels = c("UKB", "1KGphase3"),
                                                 loci = "APOE", 
                                                 file_types = "multi_finemap", 
                                                 results_dir = "./data", 
                                                 overwrite = F)
## [1] "Fetching echolocatoR Fine-mapping Portal study metadata."
## [1] "+ 4 datasets remain after filtering."
## [1] "+ Searching for multi_finemap files..."
## [1] "+ 2 unique files identified."
## [1] "+ Downloading 2 files..."
## [1] "+ Returning local file paths."

Merge fine-mapping results

finemap_dat <- lapply(local_finemap,function(x){
  dataset <- basename(dirname(dirname(dirname(x))))
  LD_ref <- stringr::str_split(basename(x),pattern = "[.]")[[1]][2]
  printer("Importing",dataset)
  dat <- data.table::fread(x) 
  cbind(dataset=dataset, 
        LD_ref=LD_ref, 
        dat)
}) %>% data.table::rbindlist()
## [1] "Importing Jansen_2018"
## [1] "Importing Marioni_2018"

Identify credible sets

Show just the Union Credible Set SNPs (SNPs nominated by at least 1/4 fine-mapping tools).

ucs_snps <- subset(finemap_dat, Support>0)
createDT(ucs_snps)

Identify Consensus SNPs

Filter to just the consensus SNPs (SNPs nominated by 2 or more fine-mapping tools).

consensus_snps <- subset(finemap_dat, Consensus_SNP==T)
createDT(consensus_snps)

Cell-type-specific annotations

Epigenomic peak overlap

finemap_dat$Gene<- finemap_dat$Locus
finemap_dat$Locus <- paste(finemap_dat$Locus,finemap_dat$dataset,sep="_")
plot_res <- echolocatoR::SUMMARISE.peak_overlap_plot(merged_DT = finemap_dat,
                                                     snp_filter = "Support>0",
                                                     include.NOTT_2019_peaks = T,
                                        include.NOTT_2019_enhancers_promoters = T,
                                        include.NOTT_2019_PLACseq = T,
                                        include.CORCES_2020_scATACpeaks = T,
                                        include.CORCES_2020_Cicero_coaccess = T,
                                        include.CORCES_2020_bulkATACpeaks = T,
                                        include.CORCES_2020_HiChIP_FitHiChIP_coaccess = T,
                                      include.CORCES_2020_gene_annotations = T,
                                      plot_celltype_specificity = T,
                                      plot_celltype_specificity_genes = T)
## [1] "++ NOTT_2019:: Downloading and merging 12 peaks BED files."
## [1] "++ NOTT_2019:: Converting merged BED files to GRanges."
## [1] "++ NOTT_2019:: 634540 ranges retrieved."
## [1] "Importing Astrocyte enhancers ..."
## [1] "Importing Astrocyte promoters ..."
## [1] "Importing Neuronal enhancers ..."
## [1] "Importing Neuronal promoters ..."
## [1] "Importing Oligo enhancers ..."
## [1] "Importing Oligo promoters ..."
## [1] "Importing Microglia enhancers ..."
## [1] "Importing Microglia promoters ..."
## [1] "++ Converting to GRanges."
## [1] "Importing Microglia interactome ..."
## [1] "Importing Neuronal interactome ..."
## [1] "Importing Oligo interactome ..."
## [1] "CORCES_2020:: Extracting overlapping cell-type-specific scATAC-seq peaks"
## Error in `[[<-`(`*tmp*`, name, value = "scATAC") : 
##   1 elements in value to replace 0 elements
## [1] "CORCES_2020:: Extracting overlapping bulkATAC-seq peaks from brain tissue"
## Error in `[[<-`(`*tmp*`, name, value = "bulkATAC") : 
##   1 elements in value to replace 0 elements

Show the epigenomic overlap results

createDT(plot_res$data)

Summarise assay counts

Summarise the number of assays in which at least one UCS SNP overlapped with a cell-type-specific epigenomic peak.

celltype_counts <- plot_res$data %>% 
  dplyr::group_by(Locus, Cell_type) %>%
  dplyr::summarise(Count=sum(Count,na.rm = T)) %>%
  dplyr::arrange(Locus, desc(Count))
## `summarise()` has grouped output by 'Locus'. You can override using the `.groups` argument.
createDT(celltype_counts)

SNP-level results

The above reports are useful to see thing in aggregate, but here we look at the SNP-level results.

  • First, we extract cell-type-specific regulatory elements identified in either Nott et al. 2019 or Corces et al. 2020, that overlap with fine-mapped union credible set SNPs (Support>0).

  • Next, we separately extract cell-type-specific interactome anchors that overlap with those regulatory elements. These can be used to link a given regulatory element (e.g. enhancer) to a specific gene (or set of genes) that it affects.

gr.gre <- SUMMARISE.peak_overlap(merged_DT=finemap_dat,
                                   snp_filter="Support>0",
                                   include.NOTT_2019_peaks=T,
                                   include.NOTT_2019_enhancers_promoters=T,
                                   include.NOTT_2019_PLACseq=T,
                                   include.CORCES_2020_scATACpeaks=T,
                                   include.CORCES_2020_Cicero_coaccess=T,
                                   include.CORCES_2020_bulkATACpeaks=T,
                                   include.CORCES_2020_HiChIP_FitHiChIP_coaccess=T,
                                   include.CORCES_2020_gene_annotations=T)  
## [1] "++ NOTT_2019:: Downloading and merging 12 peaks BED files."
## [1] "++ NOTT_2019:: Converting merged BED files to GRanges."
## [1] "++ NOTT_2019:: 634540 ranges retrieved."
## 10 query SNP(s) detected with reference overlap.
## [1] "Importing Astrocyte enhancers ..."
## [1] "Importing Astrocyte promoters ..."
## [1] "Importing Neuronal enhancers ..."
## [1] "Importing Neuronal promoters ..."
## [1] "Importing Oligo enhancers ..."
## [1] "Importing Oligo promoters ..."
## [1] "Importing Microglia enhancers ..."
## [1] "Importing Microglia promoters ..."
## [1] "++ Converting to GRanges."
## 16 query SNP(s) detected with reference overlap.
## [1] "Importing Microglia interactome ..."
## [1] "Importing Neuronal interactome ..."
## [1] "Importing Oligo interactome ..."
## 41 query SNP(s) detected with reference overlap.
## 13 query SNP(s) detected with reference overlap.
## [1] "CORCES_2020:: Extracting overlapping cell-type-specific scATAC-seq peaks"
## Start at 2021-04-09 00:32:56
## 
## 
## End at 2021-04-09 00:32:59
## Runtime in total is: 3 secs
## 0 query SNP(s) detected with reference overlap.
## Error in `[[<-`(`*tmp*`, name, value = "scATAC") : 
##   1 elements in value to replace 0 elements
## [1] "CORCES_2020:: Extracting overlapping bulkATAC-seq peaks from brain tissue"
## Start at 2021-04-09 00:32:59
## 
## 
## End at 2021-04-09 00:33:00
## Runtime in total is: 1 secs
## 0 query SNP(s) detected with reference overlap.
## Error in `[[<-`(`*tmp*`, name, value = "bulkATAC") : 
##   1 elements in value to replace 0 elements
## [1] "80 hits across 5 assays in 1 studies found."
gr.elements <- subset(gr.gre, !Assay %in% c("HiChIP_FitHiChIP","PLAC"))
non_na_cols <- colnames(gr.elements@elementMetadata)[colSums(!is.na(gr.elements@elementMetadata))>0]

gr.anchors <- subset(gr.gre, Assay %in% c("HiChIP_FitHiChIP","PLAC"))

gr.hits <- clean_granges(GRanges_overlap(GenomicRanges::granges(gr.anchors), 
                                         gr.elements[,non_na_cols],
                                         return_merged = F, verbose = T))
## [1] "+ dat1 already in GRanges format"
## [1] "+ dat2 already in GRanges format"
## 304 query SNP(s) detected with reference overlap.
gr.hits$width <- GenomicRanges::width(gr.hits)
unique(gr.elements$Assay)
## [1] "H3K27ac"   "H3K4me3"   "enhancers" "promoters"
unique(gr.elements$dataset)
## [1] "Jansen_2018"  "Marioni_2018"
snp_overlap_counts <- data.frame(gr.hits) %>%   
  dplyr::group_by(Locus, SNP, Cell_type, Consensus_SNP, Support) %>%
  dplyr::summarise(Assay_count=dplyr::n_distinct(Assay)) %>%
   dplyr::arrange(desc(Consensus_SNP), desc(Support), desc(Assay_count))
## `summarise()` has grouped output by 'Locus', 'SNP', 'Cell_type', 'Consensus_SNP'. You can override using the `.groups` argument.
createDT(snp_overlap_counts)

Gene annotation

Now that we have the hits between fine-mapped SNPs and cell-type-specific regulatory elements, we can try linking those regulatory elements to specific genes.

Fine-mapped SNP overlap with promoters

Find fine-mapped SNPs that fall within regulatory elements (TTS, intron, promoter, promoter-TSS) connected to specific genes (active in one, some, or all cell-types).

promoters_all <-  echolocatoR::NOTT_2019.interactome$H3K4me3_around_TSS_annotated_pe

gr.promoters <- GenomicRanges::makeGRangesFromDataFrame(promoters_all,
                                                        seqnames.field = "chr", 
                                                        start.field = "start", 
                                                        end.field = "end", 
                                                        keep.extra.columns = T) 
gr.overlap1 <- clean_granges(GRanges_overlap(gr.hits, gr.promoters))
## 364 query SNP(s) detected with reference overlap.
gr.overlap1$Assay <- "interactome_H3K4me3_around_TSS"
gr.overlap1$Element_old <- gr.overlap1$Element
gr.overlap1$Element <- stringr::str_split(gr.overlap1$Annotation," ", simplify = T)[,1]

Fine-mapped SNP overlap with enhancers linked to promoters

Find fine-mapped SNPs that fall within enhancers, superenhancers or promoters that interact with promoters (active in one, some or all cell-types).

range_cols <- grep("interactions$", colnames(promoters_all), value = T)
print(paste(length(range_cols),"interacting element columns identified."))
## [1] "9 interacting element columns identified."
print(range_cols)
## [1] "NeuN_enhancer_interactions"       "NeuN_promoter_interactions"      
## [3] "NeuN_superenhancer_interactions"  "PU1_enhancer_interactions"       
## [5] "PU1_promoter_interactions"        "PU1_superenhancer_interactions"  
## [7] "Olig2_enhancer_interactions"      "Olig2_promoter_interactions"     
## [9] "Olig2_superenhancer_interactions"
dat <- data.table::melt.data.table(data.table::data.table(promoters_all),
                            variable.name = "interaction_type",
                            measure.vars = range_cols) %>% 
  tidyr::separate(col = "value", sep =",|, ", into=paste0("range",1:200))
## Warning: Expected 200 pieces. Missing pieces filled with `NA` in 53992 rows [1,
## 2, 8, 9, 13, 14, 25, 35, 36, 39, 42, 44, 45, 46, 54, 60, 61, 62, 63, 65, ...].
non_na_cols <- colnames(dat)[colSums(!is.na(dat))>0]

promoter_interactions <- dat %>% 
  dplyr::select(non_na_cols) %>%
  data.table::melt.data.table(measure.vars = grep("^range",colnames(.), value = T)
                              ) %>%
  subset(!is.na(value) & (value!="")) %>%
  tidyr::separate(col = "value", sep="[:]|-", into=c("CHR","START","END")) %>%
  tidyr::separate(col = "interaction_type", sep="_", into=c("Marker","Element","Assay"),remove = F) %>%
  dplyr::mutate(START=as.numeric(START), END=as.numeric(END),
                peak_coordinates=paste0(Chr,":",Start,"-",End)) %>%
  dplyr::select(-Chr,-Start,-End) %>%
  subset(END>=START)
## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(non_na_cols)` instead of `non_na_cols` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
gr.anchor_bins <- GenomicRanges::makeGRangesFromDataFrame(promoter_interactions, 
                                        seqnames.field = "CHR", 
                                        start.field = "START", 
                                        end.field = "END", 
                                        keep.extra.columns = T)
### Convert markers to Celltypes
cell_dict <- list(neurons="NeuN",
                    microglia="PU1",
                    oligo="Olig2",
                    astrocytes="LHX2",
                    periph="peripheral PU1+")
cell_dict_invert <- setNames(names(cell_dict), cell_dict)
gr.anchor_bins$Cell_type_anchor <- cell_dict_invert[gr.anchor_bins$Marker]
### IMPORTANT!: Return the gr.hits object, not the gr.anchor_bins objects
gr.overlap2 <- GRanges_overlap(gr.anchor_bins[,c("Cell_type_anchor","interaction_type","Element","Gene Name","PeakID","Distance to TSS","Nearest PromoterID","Annotation","Detailed Annotation")],
                                             gr.hits[,c("dataset","Locus","SNP","Support","Consensus_SNP","Cell_type","Assay")], 
                                             return_merged = T) %>%
  clean_granges() %>%
  subset(Cell_type==Cell_type_anchor)
## 46894 query SNP(s) detected with reference overlap.
## Remove rows that disagree between annotations
# gr.overlap2 <- subset(gr.overlap2, !(Element=="promoters") & (Element.1!="promoter"))
# gr.overlap2$Element_old <- gr.overlap2$Element
# gr.overlap2$Element  <- gr.overlap2$Element.1
# gr.overlap2$Assay <- "interactome_anchor_bin"

Nominate genes

Nominate causal genes based on the overlap between fine-mapped variants, active regulatory elements, and interactome data.

For robust variant nomination, we find the intersection between the following groups:

  1. Fine-mapped Consensus SNP AD GWAS variants in the APOE locus (from echolocatoR).
  2. Coordinates of individual cell-type-specific enhancers/promoters (Table S5 tab 1).
  3. Coordinates of clusters of enhancers/superenhancers/promoters (Table S5 tab 1).
  4. Coordinates of individual cell-type-specific interactome anchors (Table S5 tabs 5-12).
  5. Coordinates of individual promoters/promoter-TSS/TSS/introns with PLAC-seq interactions (Table S5 tab 1).

Table S5 comes from the supplementary material in Nott et al. 2019.

# gr.top_hits <- GRanges_overlap(gr.anchor_bins, gr.promoters)
gr.anchors$Cell_type_interactome <- gr.anchors$Cell_type
interactome_cols <- c("count","fdr","ClusterSize","ClusterType","ClusterNegLog10P",
                      "Cell_type_interactome","Anchor")
gr.overlap <- GRanges_overlap(gr.anchors[,interactome_cols],
                              c(gr.overlap1, gr.overlap2),
                              return_merged = T) %>%
  unique() %>%
  # Make sure the data overlaps with a celltype in the interactome
  subset(!is.na(Cell_type_interactome)) %>%
  # make sure celltypes are matching
  subset(Cell_type==Cell_type_interactome)
## 94020 query SNP(s) detected with reference overlap.
regulatory_overlap <- data.frame(gr.overlap) %>% 
  # subset(Consensus_SNP==T) %>%
   dplyr::select(all_of(c("dataset", "Locus","SNP","Support", "Gene.Name",
                 "Assay","Element","Cell_type", interactome_cols,
                 "Annotation","PeakID","Distance.to.TSS"
                  # grep("promoter|enhancer|interactions",
                  #      colnames(gr.overlap@elementMetadata), value = T)
                  )), -Cell_type_interactome
                )  %>%
  tidyr::separate(col="Locus", into = c("Locus"), sep = "_", extra = "drop") %>%
  unique() %>%
  # dplyr::arrange(desc(ClusterNegLog10P), ClusterSize) %>%
  # dplyr::group_by(dataset, Locus, SNP, Cell_type, Element) %>%
  # dplyr::slice_max(order_by = "count", n=1, with_ties = T) %>%
  # dplyr::slice_head() %>%
  dplyr::arrange(desc(Support)) %>%
  dplyr::mutate(Assay=gsub("promoters|enhancers","called_elements", Assay)) %>%
  subset(Support>2) %>%
  data.table::data.table()


createDT(regulatory_overlap)
# regulatory_overlap

Session info

sessioninfo::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 4.0.4 (2021-02-15)
##  os       macOS Big Sur 10.16         
##  system   x86_64, darwin17.0          
##  ui       X11                         
##  language (EN)                        
##  collate  en_GB.UTF-8                 
##  ctype    en_GB.UTF-8                 
##  tz       Europe/London               
##  date     2021-04-09                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package              * version  date       lib source        
##  AnnotationDbi          1.52.0   2020-10-27 [1] Bioconductor  
##  AnnotationFilter       1.14.0   2020-10-27 [1] Bioconductor  
##  ape                    5.4-1    2020-08-13 [1] CRAN (R 4.0.2)
##  askpass                1.1      2019-01-13 [1] CRAN (R 4.0.2)
##  assertthat             0.2.1    2019-03-21 [1] CRAN (R 4.0.2)
##  backports              1.2.1    2020-12-09 [1] CRAN (R 4.0.2)
##  base64enc              0.1-3    2015-07-28 [1] CRAN (R 4.0.2)
##  Biobase                2.50.0   2020-10-27 [1] Bioconductor  
##  BiocFileCache          1.14.0   2020-10-27 [1] Bioconductor  
##  BiocGenerics           0.36.0   2020-10-27 [1] Bioconductor  
##  BiocManager            1.30.10  2019-11-16 [1] CRAN (R 4.0.2)
##  BiocParallel           1.24.1   2020-11-06 [1] Bioconductor  
##  biomaRt                2.46.3   2021-02-11 [1] Bioconductor  
##  Biostrings             2.58.0   2020-10-27 [1] Bioconductor  
##  biovizBase             1.38.0   2020-10-27 [1] Bioconductor  
##  bit                    4.0.4    2020-08-04 [1] CRAN (R 4.0.2)
##  bit64                  4.0.5    2020-08-30 [1] CRAN (R 4.0.2)
##  bitops                 1.0-6    2013-08-17 [1] CRAN (R 4.0.2)
##  blob                   1.2.1    2020-01-20 [1] CRAN (R 4.0.2)
##  boot                   1.3-27   2021-02-12 [1] CRAN (R 4.0.2)
##  BSgenome               1.58.0   2020-10-27 [1] Bioconductor  
##  bslib                  0.2.4    2021-01-25 [1] CRAN (R 4.0.2)
##  cachem                 1.0.4    2021-02-13 [1] CRAN (R 4.0.2)
##  checkmate              2.0.0    2020-02-06 [1] CRAN (R 4.0.2)
##  class                  7.3-18   2021-01-24 [1] CRAN (R 4.0.4)
##  cli                    2.3.1    2021-02-23 [1] CRAN (R 4.0.2)
##  cluster                2.1.1    2021-02-14 [1] CRAN (R 4.0.2)
##  colorspace             2.0-0    2020-11-11 [1] CRAN (R 4.0.2)
##  crayon                 1.4.1    2021-02-08 [1] CRAN (R 4.0.2)
##  crosstalk              1.1.1    2021-01-12 [1] CRAN (R 4.0.2)
##  curl                   4.3      2019-12-02 [1] CRAN (R 4.0.1)
##  data.table             1.13.6   2020-12-30 [1] CRAN (R 4.0.4)
##  DBI                    1.1.1    2021-01-15 [1] CRAN (R 4.0.2)
##  dbplyr                 2.1.0    2021-02-03 [1] CRAN (R 4.0.2)
##  DelayedArray           0.16.3   2021-03-24 [1] Bioconductor  
##  DescTools              0.99.40  2021-02-03 [1] CRAN (R 4.0.2)
##  dichromat              2.0-0    2013-01-24 [1] CRAN (R 4.0.2)
##  digest                 0.6.27   2020-10-24 [1] CRAN (R 4.0.2)
##  dnet                   1.1.7    2020-02-20 [1] CRAN (R 4.0.2)
##  dplyr                * 1.0.5    2021-03-05 [1] CRAN (R 4.0.2)
##  DT                     0.17     2021-01-06 [1] CRAN (R 4.0.2)
##  e1071                  1.7-6    2021-03-18 [1] CRAN (R 4.0.2)
##  echolocatoR          * 0.1.2    2021-04-08 [1] local         
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##  fansi                  0.4.2    2021-01-15 [1] CRAN (R 4.0.2)
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##  hexbin                 1.28.2   2021-01-08 [1] CRAN (R 4.0.2)
##  highr                  0.8      2019-03-20 [1] CRAN (R 4.0.2)
##  Hmisc                  4.5-0    2021-02-28 [1] CRAN (R 4.0.2)
##  hms                    1.0.0    2021-01-13 [1] CRAN (R 4.0.2)
##  htmlTable              2.1.0    2020-09-16 [1] CRAN (R 4.0.2)
##  htmltools              0.5.1.1  2021-01-22 [1] CRAN (R 4.0.2)
##  htmlwidgets            1.5.3    2020-12-10 [1] CRAN (R 4.0.2)
##  httr                   1.4.2    2020-07-20 [1] CRAN (R 4.0.2)
##  igraph                 1.2.6    2020-10-06 [1] CRAN (R 4.0.2)
##  IRanges                2.24.1   2020-12-12 [1] Bioconductor  
##  jpeg                   0.1-8.1  2019-10-24 [1] CRAN (R 4.0.2)
##  jquerylib              0.1.3    2020-12-17 [1] CRAN (R 4.0.2)
##  jsonlite               1.7.2    2020-12-09 [1] CRAN (R 4.0.2)
##  knitr                  1.31     2021-01-27 [1] CRAN (R 4.0.2)
##  labeling               0.4.2    2020-10-20 [1] CRAN (R 4.0.2)
##  lattice                0.20-41  2020-04-02 [1] CRAN (R 4.0.4)
##  latticeExtra           0.6-29   2019-12-19 [1] CRAN (R 4.0.2)
##  lazyeval               0.2.2    2019-03-15 [1] CRAN (R 4.0.2)
##  lifecycle              1.0.0    2021-02-15 [1] CRAN (R 4.0.3)
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##  magrittr               2.0.1    2020-11-17 [1] CRAN (R 4.0.2)
##  MASS                   7.3-53.1 2021-02-12 [1] CRAN (R 4.0.2)
##  Matrix                 1.3-2    2021-01-06 [1] CRAN (R 4.0.4)
##  MatrixGenerics         1.2.1    2021-01-30 [1] Bioconductor  
##  matrixStats            0.58.0   2021-01-29 [1] CRAN (R 4.0.2)
##  memoise                2.0.0    2021-01-26 [1] CRAN (R 4.0.2)
##  munsell                0.5.0    2018-06-12 [1] CRAN (R 4.0.2)
##  mvtnorm                1.1-1    2020-06-09 [1] CRAN (R 4.0.2)
##  nlme                   3.1-152  2021-02-04 [1] CRAN (R 4.0.4)
##  nnet                   7.3-15   2021-01-24 [1] CRAN (R 4.0.4)
##  openssl                1.4.3    2020-09-18 [1] CRAN (R 4.0.2)
##  OrganismDbi            1.32.0   2020-10-27 [1] Bioconductor  
##  patchwork            * 1.1.1    2020-12-17 [1] CRAN (R 4.0.2)
##  pillar                 1.5.1    2021-03-05 [1] CRAN (R 4.0.2)
##  pkgconfig              2.0.3    2019-09-22 [1] CRAN (R 4.0.2)
##  plyr                   1.8.6    2020-03-03 [1] CRAN (R 4.0.2)
##  png                    0.1-7    2013-12-03 [1] CRAN (R 4.0.2)
##  prettyunits            1.1.1    2020-01-24 [1] CRAN (R 4.0.2)
##  progress               1.2.2    2019-05-16 [1] CRAN (R 4.0.2)
##  ProtGenerics           1.22.0   2020-10-27 [1] Bioconductor  
##  proxy                  0.4-25   2021-03-05 [1] CRAN (R 4.0.2)
##  purrr                  0.3.4    2020-04-17 [1] CRAN (R 4.0.2)
##  R.methodsS3            1.8.1    2020-08-26 [1] CRAN (R 4.0.2)
##  R.oo                   1.24.0   2020-08-26 [1] CRAN (R 4.0.2)
##  R.utils                2.10.1   2020-08-26 [1] CRAN (R 4.0.2)
##  R6                     2.5.0    2020-10-28 [1] CRAN (R 4.0.2)
##  rappdirs               0.3.3    2021-01-31 [1] CRAN (R 4.0.2)
##  RBGL                   1.66.0   2020-10-27 [1] Bioconductor  
##  RCircos                1.2.1    2019-03-12 [1] CRAN (R 4.0.2)
##  RColorBrewer           1.1-2    2014-12-07 [1] CRAN (R 4.0.2)
##  Rcpp                   1.0.6    2021-01-15 [1] CRAN (R 4.0.2)
##  RCurl                  1.98-1.3 2021-03-16 [1] CRAN (R 4.0.2)
##  readr                  1.4.0    2020-10-05 [1] CRAN (R 4.0.2)
##  reshape                0.8.8    2018-10-23 [1] CRAN (R 4.0.2)
##  reshape2               1.4.4    2020-04-09 [1] CRAN (R 4.0.2)
##  Rgraphviz              2.34.0   2020-10-27 [1] Bioconductor  
##  rlang                  0.4.10   2020-12-30 [1] CRAN (R 4.0.2)
##  rmarkdown              2.7      2021-02-19 [1] CRAN (R 4.0.3)
##  rootSolve              1.8.2.1  2020-04-27 [1] CRAN (R 4.0.2)
##  rpart                  4.1-15   2019-04-12 [1] CRAN (R 4.0.4)
##  rprojroot              2.0.2    2020-11-15 [1] CRAN (R 4.0.2)
##  Rsamtools              2.6.0    2020-10-27 [1] Bioconductor  
##  RSQLite                2.2.4    2021-03-12 [1] CRAN (R 4.0.3)
##  rstudioapi             0.13     2020-11-12 [1] CRAN (R 4.0.2)
##  rtracklayer            1.50.0   2020-10-27 [1] Bioconductor  
##  S4Vectors              0.28.1   2020-12-09 [1] Bioconductor  
##  sass                   0.3.1    2021-01-24 [1] CRAN (R 4.0.2)
##  scales                 1.1.1    2020-05-11 [1] CRAN (R 4.0.2)
##  sessioninfo            1.1.1    2018-11-05 [1] CRAN (R 4.0.2)
##  stringi                1.5.3    2020-09-09 [1] CRAN (R 4.0.2)
##  stringr                1.4.0    2019-02-10 [1] CRAN (R 4.0.2)
##  SummarizedExperiment   1.20.0   2020-10-27 [1] Bioconductor  
##  supraHex               1.28.1   2020-11-24 [1] Bioconductor  
##  survival               3.2-7    2020-09-28 [1] CRAN (R 4.0.4)
##  tibble                 3.1.0    2021-02-25 [1] CRAN (R 4.0.2)
##  tidyr                  1.1.3    2021-03-03 [1] CRAN (R 4.0.2)
##  tidyselect             1.1.0    2020-05-11 [1] CRAN (R 4.0.2)
##  utf8                   1.2.1    2021-03-12 [1] CRAN (R 4.0.3)
##  VariantAnnotation      1.36.0   2020-10-28 [1] Bioconductor  
##  vctrs                  0.3.6    2020-12-17 [1] CRAN (R 4.0.2)
##  viridisLite            0.3.0    2018-02-01 [1] CRAN (R 4.0.1)
##  withr                  2.4.1    2021-01-26 [1] CRAN (R 4.0.2)
##  xfun                   0.22     2021-03-11 [1] CRAN (R 4.0.3)
##  XGR                    1.1.7    2020-01-08 [1] url           
##  XML                    3.99-0.6 2021-03-16 [1] CRAN (R 4.0.2)
##  xml2                   1.3.2    2020-04-23 [1] CRAN (R 4.0.2)
##  XVector                0.30.0   2020-10-28 [1] Bioconductor  
##  yaml                   2.2.1    2020-02-01 [1] CRAN (R 4.0.2)
##  zlibbioc               1.36.0   2020-10-28 [1] Bioconductor  
## 
## [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library